Welcome to the Eventual blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
Turning robot video into training-ready data with daft-physical-ai
A Python library built on Daft for turning robot video into training-ready data, starting with hand tracking and reward scoring as UDFs, with more to come.
Exploring Daft's Local Execution: The Swordfish Engine
Explore how Daft's Rust-powered engine executes DataFrame and SQL queries. Learn how Swordfish enables fast, streaming image processing at scale.
After the First Run
Using Daft's observability tools to uncover performance pitfalls
Making GPUs Zoom (Part 1)
How Daft is approaching large-scale model inference with advanced GPU optimizations for faster multimodal AI workloads
End-to-End Distributed PDF Processing Pipeline
Build production-ready PDF processing pipelines with distributed computing, OCR, spatial analysis, and GPU embeddings
How to Build Scalable, End-to-end Batch Inference Pipelines with Daft
Daft makes it easy to express these pipelines end-to-end, while seamlessly scaling them up to handle massive workloads.
24 Trillion Tokens, 0 Crashes: How Essential AI Built Essential-Web v1.0 with Daft
Essential AI leveraged Daft's data engine to process a massive web-scale dataset for large language model (LLM) training.
Embedding Millions of Text Documents With Qwen3
Learn how to achieve near-100% GPU utilization processing millions of text documents with Qwen3 embeddings.
Processing 300K Images Without OOM
A Streaming Solution
Multimodal Data Processing Goes Global
Daft Community is expanding to China, bridging the gap between English documentation and Chinese innovation cycles, in partnership with Bytedance Team